Managing for Data Quality is a core Data Management responsibility, be that for accuracy of data processing, accuracy of analysis and reporting or confidence and trust of customers and suppliers in you data handling. Similarly there are regulatory requirements in the GDPR, BCBS239, IDMP and other data regulations that compel organisations to ensure a high quality of data.

Data Quality Management has two key controls:

1) Data Quality at Point of Capture - As data enters your business, be that manually or automatically, you should look to test for errors and inconsistencies sometimes referred to as a Data Quality Firewall.  

Data Qualitypng.png
  • Personal Data When a client or prospect is entering their contact details you may wish to leverage contact data validation services to confirm that email addresses, phone numbers and address are valid and in a consistent format.For data that is being entered manually into online forms by your agents or your customers you may want to look at services from Loqate (PCA Predict). You can sign up for a free trial account using our partner link. Also we have made a number of integrations to these services that you may wish to access.
  • Business Information Data  When a client or prospect is entering their company details it is best practice to capture a definitive Business name and a unique ID for the business, otherwise matching data across your systems becomes a challenge when you have variations in business names.
  • Machine Data - If you are receiving or generating machine data such as transactional data, sensor data, financial instruments or log data you should look to run validation rules before committing to your data stores. These rules could be manually coded into you load scripts and are a core feature of most ETL tools.  If you would like to discuss a solution for you organisation then please drop an email.    

Point of Capture - Demonstration Applications

UK Business Validation Demo

AUS Business Validation Demo


2) Data Quality of Stored Data - Over time the quality of certain data types in storage deteriorate. In particular personal data quality drifts as people change phones numbers and email addresses, move house, or augment family configurations. 

  • Personal Data - To remedy Data Quality drift in can regularly ask contacts to verify that their data is up to date and/or augment with updates from an external contact data source such as that offered by the likes of Experian and PCA Predict.  If you would like to test the batch contact data quality update services from PCA Predict then you can access a free trial account from our partner link

Contact Data Quality Management Framework

Article 5 - Data Quality.png

If you would like to discuss your Data Quality needs and strategy then please contact us